class_docstr: CIFAR10 data set from https://www.cs.toronto.edu/~kriz/cifar.html
  
  Arguments:
      path (str): Local path to copy data files.
      normalize (bool): Flag to normalize data.
      whiten (bool): Flag to apply whitening transform.
      pad_classes (bool): Flag to pad out class count to 16
                          for compatibility with conv layers on GPU.
docstr: Subtract mean and normalize by vector norm.
docstr_tok: ["Subtract","mean","and","normalize","by","vector","norm","."]
code: def global_contrast_normalize(X, scale=1.0, min_divisor=1e-8):
      """
      Subtract mean and normalize by vector norm.
      """
  
      X = X - X.mean(axis=1)[:, np.newaxis]
  
      normalizers = np.sqrt((X**2).sum(axis=1)) / scale
      normalizers[normalizers < min_divisor] = 1.0
  
      X /= normalizers[:, np.newaxis]
  
      return X
  
imports: from __future__ import division
  from future import standard_library
  from builtins import str
  import gzip
  import logging
  import numpy
  import os
  import tarfile
  from neon.util.compat import pickle
  from neon.util.compat import pickle_load
  from neon.data.datasets import Dataset
  from neon.data.dataiterator import ArrayIterator
label_desc: Use image node_id 6 for calling the CIFAR10 obj's underlying member method code with example usage: obj.global_contrast_normalize(X, scale, min_divisor) and returns: X